Commercial enterprises (e.g., companies) and others gather, store, and analyze an increasing amount of data. In fact, the trend now is to store and archive almost all data before making a decision on whether or not to analyze the stored data. Although the per unit costs associated with storing data has declined over time, the total costs for storage has increased for many companies because of the volumes of stored data. Hence, it is important for companies to find cost-effective ways to manage their data storage environments for storing and managing large quantities of data. Companies now manage these costs by having various tiers of storage, with different costs associated with each of these tiers. Each tier can have different data storage hardware (e.g., storage processor, storage medium, storage I/O network, etc.) and different storage services (e.g., data maintenance, data integrity check, backup, snapshots, etc.). Companies can use the different tiers of storage for different types of data. As an example, to store data that is accessed frequently, companies may use a data storage tier that has high performance characteristics. On the other hand, for big data applications, companies often prefer high density and high storage volume archival storage systems, which tend to be less expensive on a per unit basis. However, it is often a challenge to keep the cost down for these archival storage systems due to the necessity of maintaining a large number of data storage devices.
The figures depict various embodiments of the disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.